Titlebook: Digital Pathology; 15th European Congre Constantino Carlos Reyes-Aldasoro,Andrew Janowczyk Conference proceedings 2019 Springer Nature Swit

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一夫一妻制

Conference proceedings 2019he 21 full papers presented in this volume were carefully reviewed and selected from 30 submissions. The congress theme will be Accelerating Clinical Deployment, with a focus on computational pathology and leveraging the power of big data and artificial intelligence to bridge the gaps between resear

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Active Learning for Patch-Based Digital Pathology Using Convolutional Neural Networks to Reduce Annoained on small patches, each containing a single nucleus. Traditional query strategies performed worse than random sampling. A K-centre sampling strategy showed a modest gain. Further investigation is needed in order to achieve significant performance gains using deep active learning for this task.

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Patch Clustering for Representation of Histopathology Imagese the same characteristics. We used a Gaussian mixture model (GMM) to represent each class with a rather small (10%–50%) portion of patches. The results showed that LBP features can outperform deep features. By selecting only 50% of all patches after SOM clustering and GMM patch selection, we receiv

Preserve

Deep Features for Tissue-Fold Detection in Histopathology Imagesgurations. Based on the leave-one-out validation strategy, we achieved . accuracy, whereas with augmentation the accuracy increased to .. We have tested the generalization of our method with five unseen WSIs from the NIH (National Cancer Institute) dataset. The accuracy for patch-wise detection was

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mechanism

Multi-tissue Partitioning for Whole Slide Images of Colorectal Cancer Histopathology Images with Dee